Overview

Dataset statistics

Number of variables20
Number of observations495410
Missing cells213
Missing cells (%)< 0.1%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory75.6 MiB
Average record size in memory160.0 B

Variable types

Numeric8
DateTime2
Categorical5
Text5

Alerts

Status is highly imbalanced (58.9%)Imbalance
Status Desc is highly imbalanced (58.9%)Imbalance
DR_NO has unique valuesUnique
Vict Age has 120799 (24.4%) zerosZeros

Reproduction

Analysis started2024-05-13 04:53:10.405867
Analysis finished2024-05-13 04:54:01.134997
Duration50.73 seconds
Software versionydata-profiling vv4.6.3
Download configurationconfig.json

Variables

DR_NO
Real number (ℝ)

UNIQUE 

Distinct495410
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.0914653 × 108
Minimum817
Maximum2.2991045 × 108
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.8 MiB
2024-05-13T09:54:01.358272image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum817
5-th percentile2.0031238 × 108
Q12.0140968 × 108
median2.1061182 × 108
Q32.1182023 × 108
95-th percentile2.2151012 × 108
Maximum2.2991045 × 108
Range2.2990963 × 108
Interquartile range (IQR)10410548

Descriptive statistics

Standard deviation7395200.2
Coefficient of variation (CV)0.035358943
Kurtosis2.5283166
Mean2.0914653 × 108
Median Absolute Deviation (MAD)8901205
Skewness0.18665432
Sum1.0361328 × 1014
Variance5.4688986 × 1013
MonotonicityNot monotonic
2024-05-13T09:54:01.631694image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
10304468 1
 
< 0.1%
211307795 1
 
< 0.1%
210905503 1
 
< 0.1%
211311004 1
 
< 0.1%
210712381 1
 
< 0.1%
220105727 1
 
< 0.1%
211704493 1
 
< 0.1%
211508484 1
 
< 0.1%
211409645 1
 
< 0.1%
211220693 1
 
< 0.1%
Other values (495400) 495400
> 99.9%
ValueCountFrequency (%)
817 1
< 0.1%
2113 1
< 0.1%
10304468 1
< 0.1%
190101086 1
< 0.1%
190101087 1
< 0.1%
190326475 1
< 0.1%
191501505 1
< 0.1%
191921269 1
< 0.1%
200100001 1
< 0.1%
200100002 1
< 0.1%
ValueCountFrequency (%)
229910450 1
< 0.1%
229906388 1
< 0.1%
229904986 1
< 0.1%
222109861 1
< 0.1%
222109860 1
< 0.1%
222109859 1
< 0.1%
222109858 1
< 0.1%
222109856 1
< 0.1%
222109855 1
< 0.1%
222109854 1
< 0.1%
Distinct881
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size3.8 MiB
Minimum2020-01-01 00:00:00
Maximum2022-12-05 00:00:00
2024-05-13T09:54:02.047658image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-05-13T09:54:02.310474image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
Distinct1439
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size3.8 MiB
Minimum2024-05-13 00:01:00
Maximum2024-05-13 23:59:00
2024-05-13T09:54:02.585675image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-05-13T09:54:03.002023image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

AREA
Real number (ℝ)

Distinct21
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean10.768563
Minimum1
Maximum21
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.8 MiB
2024-05-13T09:54:03.299354image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q16
median11
Q316
95-th percentile20
Maximum21
Range20
Interquartile range (IQR)10

Descriptive statistics

Standard deviation6.0687055
Coefficient of variation (CV)0.56355758
Kurtosis-1.1833882
Mean10.768563
Median Absolute Deviation (MAD)5
Skewness0.0049920201
Sum5334854
Variance36.829186
MonotonicityNot monotonic
2024-05-13T09:54:03.650858image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=21)
ValueCountFrequency (%)
12 31537
 
6.4%
1 31225
 
6.3%
14 29496
 
6.0%
3 27374
 
5.5%
6 26941
 
5.4%
18 25470
 
5.1%
15 25235
 
5.1%
20 24897
 
5.0%
13 24311
 
4.9%
8 23083
 
4.7%
Other values (11) 225841
45.6%
ValueCountFrequency (%)
1 31225
6.3%
2 22778
4.6%
3 27374
5.5%
4 18947
3.8%
5 21110
4.3%
6 26941
5.4%
7 23025
4.6%
8 23083
4.7%
9 20953
4.2%
10 20275
4.1%
ValueCountFrequency (%)
21 20131
4.1%
20 24897
5.0%
19 20306
4.1%
18 25470
5.1%
17 19679
4.0%
16 17066
3.4%
15 25235
5.1%
14 29496
6.0%
13 24311
4.9%
12 31537
6.4%

AREA NAME
Categorical

Distinct21
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size3.8 MiB
77th Street
 
31537
Central
 
31225
Pacific
 
29496
Southwest
 
27374
Hollywood
 
26941
Other values (16)
348837 

Length

Max length11
Median length10
Mean length8.2967058
Min length6

Characters and Unicode

Total characters4110271
Distinct characters39
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowSouthwest
2nd rowCentral
3rd rowN Hollywood
4th rowMission
5th rowCentral

Common Values

ValueCountFrequency (%)
77th Street 31537
 
6.4%
Central 31225
 
6.3%
Pacific 29496
 
6.0%
Southwest 27374
 
5.5%
Hollywood 26941
 
5.4%
Southeast 25470
 
5.1%
N Hollywood 25235
 
5.1%
Olympic 24897
 
5.0%
Newton 24311
 
4.9%
West LA 23083
 
4.7%
Other values (11) 225841
45.6%

Length

2024-05-13T09:54:04.004283image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
hollywood 52176
 
8.5%
west 43358
 
7.0%
77th 31537
 
5.1%
street 31537
 
5.1%
central 31225
 
5.1%
pacific 29496
 
4.8%
southwest 27374
 
4.4%
southeast 25470
 
4.1%
n 25235
 
4.1%
olympic 24897
 
4.0%
Other values (14) 294188
47.7%

Most occurring characters

ValueCountFrequency (%)
o 389559
 
9.5%
t 382179
 
9.3%
e 356935
 
8.7%
l 296075
 
7.2%
a 255918
 
6.2%
s 222042
 
5.4%
i 207296
 
5.0%
r 192035
 
4.7%
h 165722
 
4.0%
n 155552
 
3.8%
Other values (29) 1486958
36.2%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 3318075
80.7%
Uppercase Letter 608039
 
14.8%
Space Separator 121083
 
2.9%
Decimal Number 63074
 
1.5%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
o 389559
11.7%
t 382179
11.5%
e 356935
10.8%
l 296075
8.9%
a 255918
 
7.7%
s 222042
 
6.7%
i 207296
 
6.2%
r 192035
 
5.8%
h 165722
 
5.0%
n 155552
 
4.7%
Other values (12) 694762
20.9%
Uppercase Letter
ValueCountFrequency (%)
H 92233
15.2%
N 92070
15.1%
S 84381
13.9%
W 66383
10.9%
V 41228
6.8%
C 31225
 
5.1%
P 29496
 
4.9%
O 24897
 
4.1%
L 23083
 
3.8%
A 23083
 
3.8%
Other values (5) 99960
16.4%
Space Separator
ValueCountFrequency (%)
121083
100.0%
Decimal Number
ValueCountFrequency (%)
7 63074
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 3926114
95.5%
Common 184157
 
4.5%

Most frequent character per script

Latin
ValueCountFrequency (%)
o 389559
 
9.9%
t 382179
 
9.7%
e 356935
 
9.1%
l 296075
 
7.5%
a 255918
 
6.5%
s 222042
 
5.7%
i 207296
 
5.3%
r 192035
 
4.9%
h 165722
 
4.2%
n 155552
 
4.0%
Other values (27) 1302801
33.2%
Common
ValueCountFrequency (%)
121083
65.7%
7 63074
34.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 4110271
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
o 389559
 
9.5%
t 382179
 
9.3%
e 356935
 
8.7%
l 296075
 
7.2%
a 255918
 
6.2%
s 222042
 
5.4%
i 207296
 
5.0%
r 192035
 
4.7%
h 165722
 
4.0%
n 155552
 
3.8%
Other values (29) 1486958
36.2%

Rpt Dist No
Real number (ℝ)

Distinct1186
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1123.1374
Minimum101
Maximum2199
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.8 MiB
2024-05-13T09:54:04.357732image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum101
5-th percentile174
Q1628
median1145
Q31621
95-th percentile2069
Maximum2199
Range2098
Interquartile range (IQR)993

Descriptive statistics

Standard deviation606.94683
Coefficient of variation (CV)0.540403
Kurtosis-1.1866188
Mean1123.1374
Median Absolute Deviation (MAD)508
Skewness0.011922896
Sum5.5641351 × 108
Variance368384.46
MonotonicityNot monotonic
2024-05-13T09:54:04.744760image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
645 2576
 
0.5%
162 2475
 
0.5%
182 2227
 
0.4%
1494 2091
 
0.4%
646 2070
 
0.4%
636 2062
 
0.4%
111 1923
 
0.4%
1822 1695
 
0.3%
1555 1511
 
0.3%
1802 1474
 
0.3%
Other values (1176) 475306
95.9%
ValueCountFrequency (%)
101 450
 
0.1%
105 154
 
< 0.1%
109 15
 
< 0.1%
111 1923
0.4%
112 117
 
< 0.1%
118 401
 
0.1%
119 1407
0.3%
121 178
 
< 0.1%
122 146
 
< 0.1%
123 173
 
< 0.1%
ValueCountFrequency (%)
2199 1
 
< 0.1%
2198 20
 
< 0.1%
2197 90
 
< 0.1%
2196 232
 
< 0.1%
2189 1038
0.2%
2187 771
0.2%
2185 415
 
0.1%
2183 378
 
0.1%
2177 767
0.2%
2175 571
0.1%

Crm Cd
Real number (ℝ)

Distinct136
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean504.67497
Minimum110
Maximum956
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.8 MiB
2024-05-13T09:54:05.097717image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum110
5-th percentile230
Q1330
median480
Q3626
95-th percentile901
Maximum956
Range846
Interquartile range (IQR)296

Descriptive statistics

Standard deviation209.15906
Coefficient of variation (CV)0.41444311
Kurtosis-0.80761494
Mean504.67497
Median Absolute Deviation (MAD)149
Skewness0.43780895
Sum2.5002102 × 108
Variance43747.513
MonotonicityNot monotonic
2024-05-13T09:54:05.489693image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
510 54601
 
11.0%
624 39612
 
8.0%
330 32071
 
6.5%
740 31969
 
6.5%
310 30507
 
6.2%
230 29314
 
5.9%
440 26244
 
5.3%
626 25788
 
5.2%
354 25295
 
5.1%
420 21146
 
4.3%
Other values (126) 178863
36.1%
ValueCountFrequency (%)
110 895
 
0.2%
113 5
 
< 0.1%
121 1986
 
0.4%
122 168
 
< 0.1%
210 17605
3.6%
220 2747
 
0.6%
230 29314
5.9%
231 697
 
0.1%
235 338
 
0.1%
236 7073
 
1.4%
ValueCountFrequency (%)
956 4138
0.8%
954 16
 
< 0.1%
951 167
 
< 0.1%
950 45
 
< 0.1%
949 65
 
< 0.1%
948 4
 
< 0.1%
946 3768
0.8%
944 13
 
< 0.1%
943 132
 
< 0.1%
942 5
 
< 0.1%
Distinct136
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size3.8 MiB
2024-05-13T09:54:05.900165image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Length

Max length56
Median length47
Mean length29.17758
Min length5

Characters and Unicode

Total characters14454865
Distinct characters42
Distinct categories8 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)< 0.1%

Sample

1st rowBATTERY - SIMPLE ASSAULT
2nd rowBATTERY - SIMPLE ASSAULT
3rd rowVANDALISM - MISDEAMEANOR ($399 OR UNDER)
4th rowVANDALISM - FELONY ($400 & OVER, ALL CHURCH VANDALISMS)
5th rowRAPE, FORCIBLE
ValueCountFrequency (%)
380228
 
16.8%
assault 136431
 
6.0%
vehicle 127190
 
5.6%
theft 107247
 
4.7%
under 72678
 
3.2%
from 69866
 
3.1%
simple 68735
 
3.0%
burglary 65054
 
2.9%
stolen 62581
 
2.8%
petty 56725
 
2.5%
Other values (236) 1111221
49.2%
2024-05-13T09:54:06.412204image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1770822
 
12.3%
E 1246981
 
8.6%
T 1072993
 
7.4%
A 1052029
 
7.3%
L 826449
 
5.7%
R 810602
 
5.6%
S 680797
 
4.7%
I 638224
 
4.4%
N 618218
 
4.3%
O 592837
 
4.1%
Other values (32) 5144913
35.6%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 11182969
77.4%
Space Separator 1770822
 
12.3%
Decimal Number 491235
 
3.4%
Other Punctuation 295438
 
2.0%
Dash Punctuation 288310
 
2.0%
Open Punctuation 144597
 
1.0%
Close Punctuation 144005
 
1.0%
Currency Symbol 137489
 
1.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
E 1246981
 
11.2%
T 1072993
 
9.6%
A 1052029
 
9.4%
L 826449
 
7.4%
R 810602
 
7.2%
S 680797
 
6.1%
I 638224
 
5.7%
N 618218
 
5.5%
O 592837
 
5.3%
D 431876
 
3.9%
Other values (15) 3211963
28.7%
Decimal Number
ValueCountFrequency (%)
0 190489
38.8%
9 120927
24.6%
5 90927
18.5%
1 38448
 
7.8%
4 32879
 
6.7%
3 16989
 
3.5%
7 528
 
0.1%
2 48
 
< 0.1%
Other Punctuation
ValueCountFrequency (%)
, 145920
49.4%
& 107814
36.5%
. 36152
 
12.2%
/ 5552
 
1.9%
Space Separator
ValueCountFrequency (%)
1770822
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 288310
100.0%
Open Punctuation
ValueCountFrequency (%)
( 144597
100.0%
Close Punctuation
ValueCountFrequency (%)
) 144005
100.0%
Currency Symbol
ValueCountFrequency (%)
$ 137489
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 11182969
77.4%
Common 3271896
 
22.6%

Most frequent character per script

Latin
ValueCountFrequency (%)
E 1246981
 
11.2%
T 1072993
 
9.6%
A 1052029
 
9.4%
L 826449
 
7.4%
R 810602
 
7.2%
S 680797
 
6.1%
I 638224
 
5.7%
N 618218
 
5.5%
O 592837
 
5.3%
D 431876
 
3.9%
Other values (15) 3211963
28.7%
Common
ValueCountFrequency (%)
1770822
54.1%
- 288310
 
8.8%
0 190489
 
5.8%
, 145920
 
4.5%
( 144597
 
4.4%
) 144005
 
4.4%
$ 137489
 
4.2%
9 120927
 
3.7%
& 107814
 
3.3%
5 90927
 
2.8%
Other values (7) 130596
 
4.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 14454865
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1770822
 
12.3%
E 1246981
 
8.6%
T 1072993
 
7.4%
A 1052029
 
7.3%
L 826449
 
5.7%
R 810602
 
5.6%
S 680797
 
4.7%
I 638224
 
4.4%
N 618218
 
4.3%
O 592837
 
4.1%
Other values (32) 5144913
35.6%
Distinct178790
Distinct (%)36.1%
Missing0
Missing (%)0.0%
Memory size3.8 MiB
2024-05-13T09:54:06.831173image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Length

Max length49
Median length34
Mean length14.641164
Min length1

Characters and Unicode

Total characters7253379
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique157361 ?
Unique (%)31.8%

Sample

1st row0444 0913
2nd row0416 1822 1414
3rd row0329 1402
4th row0329
5th row0413 1822 1262 1415
ValueCountFrequency (%)
1822 160541
 
10.1%
0344 146742
 
9.2%
0913 81694
 
5.1%
0329 69525
 
4.4%
0 68967
 
4.3%
0416 66004
 
4.1%
1300 50074
 
3.1%
2000 41162
 
2.6%
0400 39231
 
2.5%
1402 32850
 
2.1%
Other values (689) 834348
52.4%
2024-05-13T09:54:07.833448image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 1694933
23.4%
1095728
15.1%
1 961604
13.3%
2 851482
11.7%
4 841841
11.6%
3 709840
9.8%
9 310147
 
4.3%
8 300873
 
4.1%
6 230600
 
3.2%
5 158923
 
2.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 6157651
84.9%
Space Separator 1095728
 
15.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 1694933
27.5%
1 961604
15.6%
2 851482
13.8%
4 841841
13.7%
3 709840
11.5%
9 310147
 
5.0%
8 300873
 
4.9%
6 230600
 
3.7%
5 158923
 
2.6%
7 97408
 
1.6%
Space Separator
ValueCountFrequency (%)
1095728
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 7253379
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 1694933
23.4%
1095728
15.1%
1 961604
13.3%
2 851482
11.7%
4 841841
11.6%
3 709840
9.8%
9 310147
 
4.3%
8 300873
 
4.1%
6 230600
 
3.2%
5 158923
 
2.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 7253379
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 1694933
23.4%
1095728
15.1%
1 961604
13.3%
2 851482
11.7%
4 841841
11.6%
3 709840
9.8%
9 310147
 
4.3%
8 300873
 
4.1%
6 230600
 
3.2%
5 158923
 
2.2%

Vict Age
Real number (ℝ)

ZEROS 

Distinct101
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean29.950849
Minimum-1
Maximum120
Zeros120799
Zeros (%)24.4%
Negative14
Negative (%)< 0.1%
Memory size3.8 MiB
2024-05-13T09:54:08.178656image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum-1
5-th percentile0
Q111
median31
Q345
95-th percentile65
Maximum120
Range121
Interquartile range (IQR)34

Descriptive statistics

Standard deviation21.71389
Coefficient of variation (CV)0.72498413
Kurtosis-0.76194068
Mean29.950849
Median Absolute Deviation (MAD)15
Skewness0.1038918
Sum14837950
Variance471.49302
MonotonicityNot monotonic
2024-05-13T09:54:08.526495image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 120799
24.4%
30 11355
 
2.3%
35 11043
 
2.2%
29 10840
 
2.2%
31 10816
 
2.2%
28 10774
 
2.2%
32 10455
 
2.1%
27 10213
 
2.1%
26 10205
 
2.1%
33 9982
 
2.0%
Other values (91) 278928
56.3%
ValueCountFrequency (%)
-1 14
 
< 0.1%
0 120799
24.4%
2 229
 
< 0.1%
3 251
 
0.1%
4 286
 
0.1%
5 304
 
0.1%
6 286
 
0.1%
7 306
 
0.1%
8 310
 
0.1%
9 374
 
0.1%
ValueCountFrequency (%)
120 1
 
< 0.1%
99 172
< 0.1%
98 39
 
< 0.1%
97 33
 
< 0.1%
96 44
 
< 0.1%
95 51
 
< 0.1%
94 63
 
< 0.1%
93 58
 
< 0.1%
92 75
< 0.1%
91 118
< 0.1%

Vict Sex
Categorical

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size3.8 MiB
M
208163 
F
181122 
X
106069 
H
 
56

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters495410
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowF
2nd rowM
3rd rowF
4th rowX
5th rowF

Common Values

ValueCountFrequency (%)
M 208163
42.0%
F 181122
36.6%
X 106069
21.4%
H 56
 
< 0.1%

Length

2024-05-13T09:54:08.853855image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-13T09:54:09.088949image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
ValueCountFrequency (%)
m 208163
42.0%
f 181122
36.6%
x 106069
21.4%
h 56
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
M 208163
42.0%
F 181122
36.6%
X 106069
21.4%
H 56
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 495410
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
M 208163
42.0%
F 181122
36.6%
X 106069
21.4%
H 56
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
Latin 495410
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
M 208163
42.0%
F 181122
36.6%
X 106069
21.4%
H 56
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 495410
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
M 208163
42.0%
F 181122
36.6%
X 106069
21.4%
H 56
 
< 0.1%

Vict Descent
Categorical

Distinct19
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size3.8 MiB
H
151736 
X
110838 
W
103880 
B
71051 
O
39349 
Other values (14)
18556 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters495410
Distinct characters19
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowB
2nd rowH
3rd rowW
4th rowX
5th rowH

Common Values

ValueCountFrequency (%)
H 151736
30.6%
X 110838
22.4%
W 103880
21.0%
B 71051
14.3%
O 39349
 
7.9%
A 10611
 
2.1%
K 2557
 
0.5%
F 1863
 
0.4%
C 1576
 
0.3%
J 613
 
0.1%
Other values (9) 1336
 
0.3%

Length

2024-05-13T09:54:09.348526image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
h 151736
30.6%
x 110838
22.4%
w 103880
21.0%
b 71051
14.3%
o 39349
 
7.9%
a 10611
 
2.1%
k 2557
 
0.5%
f 1863
 
0.4%
c 1576
 
0.3%
j 613
 
0.1%
Other values (9) 1336
 
0.3%

Most occurring characters

ValueCountFrequency (%)
H 151736
30.6%
X 110838
22.4%
W 103880
21.0%
B 71051
14.3%
O 39349
 
7.9%
A 10611
 
2.1%
K 2557
 
0.5%
F 1863
 
0.4%
C 1576
 
0.3%
J 613
 
0.1%
Other values (9) 1336
 
0.3%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 495410
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
H 151736
30.6%
X 110838
22.4%
W 103880
21.0%
B 71051
14.3%
O 39349
 
7.9%
A 10611
 
2.1%
K 2557
 
0.5%
F 1863
 
0.4%
C 1576
 
0.3%
J 613
 
0.1%
Other values (9) 1336
 
0.3%

Most occurring scripts

ValueCountFrequency (%)
Latin 495410
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
H 151736
30.6%
X 110838
22.4%
W 103880
21.0%
B 71051
14.3%
O 39349
 
7.9%
A 10611
 
2.1%
K 2557
 
0.5%
F 1863
 
0.4%
C 1576
 
0.3%
J 613
 
0.1%
Other values (9) 1336
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 495410
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
H 151736
30.6%
X 110838
22.4%
W 103880
21.0%
B 71051
14.3%
O 39349
 
7.9%
A 10611
 
2.1%
K 2557
 
0.5%
F 1863
 
0.4%
C 1576
 
0.3%
J 613
 
0.1%
Other values (9) 1336
 
0.3%
Distinct306
Distinct (%)0.1%
Missing213
Missing (%)< 0.1%
Memory size3.8 MiB
2024-05-13T09:54:10.448031image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Length

Max length63
Median length56
Mean length17.45013
Min length4

Characters and Unicode

Total characters8641252
Distinct characters45
Distinct categories7 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique6 ?
Unique (%)< 0.1%

Sample

1st rowSINGLE FAMILY DWELLING
2nd rowSIDEWALK
3rd rowMULTI-UNIT DWELLING (APARTMENT, DUPLEX, ETC)
4th rowBEAUTY SUPPLY STORE
5th rowNIGHT CLUB (OPEN EVENINGS ONLY)
ValueCountFrequency (%)
dwelling 141248
12.8%
street 126701
 
11.5%
single 81886
 
7.4%
family 81638
 
7.4%
etc 60975
 
5.5%
multi-unit 59610
 
5.4%
apartment 59610
 
5.4%
duplex 59610
 
5.4%
parking 42103
 
3.8%
lot 37187
 
3.4%
Other values (491) 352040
31.9%
2024-05-13T09:54:11.759692image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
E 992147
 
11.5%
T 790170
 
9.1%
L 721739
 
8.4%
I 646971
 
7.5%
607411
 
7.0%
N 532659
 
6.2%
R 469294
 
5.4%
A 448747
 
5.2%
S 442501
 
5.1%
G 342113
 
4.0%
Other values (35) 2647500
30.6%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 7590920
87.8%
Space Separator 607411
 
7.0%
Other Punctuation 220501
 
2.6%
Open Punctuation 74159
 
0.9%
Close Punctuation 73309
 
0.8%
Dash Punctuation 68622
 
0.8%
Decimal Number 6330
 
0.1%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
E 992147
13.1%
T 790170
10.4%
L 721739
 
9.5%
I 646971
 
8.5%
N 532659
 
7.0%
R 469294
 
6.2%
A 448747
 
5.9%
S 442501
 
5.8%
G 342113
 
4.5%
D 299415
 
3.9%
Other values (16) 1905164
25.1%
Decimal Number
ValueCountFrequency (%)
0 2109
33.3%
3 2100
33.2%
9 1618
25.6%
7 314
 
5.0%
1 95
 
1.5%
2 38
 
0.6%
4 38
 
0.6%
8 18
 
0.3%
Other Punctuation
ValueCountFrequency (%)
, 147431
66.9%
/ 64795
29.4%
* 2628
 
1.2%
' 2379
 
1.1%
. 2127
 
1.0%
& 1013
 
0.5%
: 128
 
0.1%
Space Separator
ValueCountFrequency (%)
607411
100.0%
Open Punctuation
ValueCountFrequency (%)
( 74159
100.0%
Close Punctuation
ValueCountFrequency (%)
) 73309
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 68622
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 7590920
87.8%
Common 1050332
 
12.2%

Most frequent character per script

Latin
ValueCountFrequency (%)
E 992147
13.1%
T 790170
10.4%
L 721739
 
9.5%
I 646971
 
8.5%
N 532659
 
7.0%
R 469294
 
6.2%
A 448747
 
5.9%
S 442501
 
5.8%
G 342113
 
4.5%
D 299415
 
3.9%
Other values (16) 1905164
25.1%
Common
ValueCountFrequency (%)
607411
57.8%
, 147431
 
14.0%
( 74159
 
7.1%
) 73309
 
7.0%
- 68622
 
6.5%
/ 64795
 
6.2%
* 2628
 
0.3%
' 2379
 
0.2%
. 2127
 
0.2%
0 2109
 
0.2%
Other values (9) 5362
 
0.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 8641252
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
E 992147
 
11.5%
T 790170
 
9.1%
L 721739
 
8.4%
I 646971
 
7.5%
607411
 
7.0%
N 532659
 
6.2%
R 469294
 
5.4%
A 448747
 
5.2%
S 442501
 
5.1%
G 342113
 
4.0%
Other values (35) 2647500
30.6%

Weapon Used Cd
Real number (ℝ)

Distinct78
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean450.56463
Minimum101
Maximum516
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.8 MiB
2024-05-13T09:54:12.085408image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum101
5-th percentile200
Q1400
median500
Q3500
95-th percentile500
Maximum516
Range415
Interquartile range (IQR)100

Descriptive statistics

Standard deviation99.543036
Coefficient of variation (CV)0.22092954
Kurtosis4.8452787
Mean450.56463
Median Absolute Deviation (MAD)0
Skewness-2.3414034
Sum2.2321422 × 108
Variance9908.816
MonotonicityNot monotonic
2024-05-13T09:54:12.505373image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
500 336947
68.0%
400 94539
 
19.1%
511 12761
 
2.6%
102 11048
 
2.2%
109 4157
 
0.8%
106 3742
 
0.8%
200 3736
 
0.8%
207 3087
 
0.6%
512 1875
 
0.4%
307 1817
 
0.4%
Other values (68) 21701
 
4.4%
ValueCountFrequency (%)
101 744
 
0.2%
102 11048
2.2%
103 265
 
0.1%
104 188
 
< 0.1%
105 19
 
< 0.1%
106 3742
 
0.8%
107 477
 
0.1%
108 13
 
< 0.1%
109 4157
 
0.8%
110 23
 
< 0.1%
ValueCountFrequency (%)
516 21
 
< 0.1%
515 481
 
0.1%
514 83
 
< 0.1%
513 230
 
< 0.1%
512 1875
 
0.4%
511 12761
2.6%
510 75
 
< 0.1%
509 28
 
< 0.1%
508 10
 
< 0.1%
507 34
 
< 0.1%
Distinct78
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size3.8 MiB
2024-05-13T09:54:13.052201image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Length

Max length46
Median length27
Mean length28.981149
Min length3

Characters and Unicode

Total characters14357551
Distinct characters40
Distinct categories7 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique3 ?
Unique (%)< 0.1%

Sample

1st rowSTRONG-ARM (HANDS, FIST, FEET OR BODILY FORCE)
2nd rowUNKNOWN WEAPON/OTHER WEAPON
3rd rowUNKNOWN WEAPON/OTHER WEAPON
4th rowUNKNOWN WEAPON/OTHER WEAPON
5th rowUNKNOWN WEAPON/OTHER WEAPON
ValueCountFrequency (%)
unknown 341191
18.8%
weapon 336951
18.6%
weapon/other 336947
18.6%
or 98275
 
5.4%
strong-arm 94539
 
5.2%
hands 94539
 
5.2%
fist 94539
 
5.2%
feet 94539
 
5.2%
bodily 94539
 
5.2%
force 94539
 
5.2%
Other values (116) 133678
 
7.4%
2024-05-13T09:54:13.935401image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
N 1940321
13.5%
O 1760625
12.3%
E 1391709
9.7%
1318866
 
9.2%
W 1022116
 
7.1%
A 929751
 
6.5%
R 778492
 
5.4%
P 693913
 
4.8%
T 691927
 
4.8%
H 477098
 
3.3%
Other values (30) 3352733
23.4%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 12209438
85.0%
Space Separator 1318866
 
9.2%
Other Punctuation 536470
 
3.7%
Dash Punctuation 98737
 
0.7%
Open Punctuation 94560
 
0.7%
Close Punctuation 94560
 
0.7%
Decimal Number 4920
 
< 0.1%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
N 1940321
15.9%
O 1760625
14.4%
E 1391709
11.4%
W 1022116
8.4%
A 929751
7.6%
R 778492
 
6.4%
P 693913
 
5.7%
T 691927
 
5.7%
H 477098
 
3.9%
U 365508
 
3.0%
Other values (16) 2157978
17.7%
Decimal Number
ValueCountFrequency (%)
6 4700
95.5%
9 62
 
1.3%
3 61
 
1.2%
4 43
 
0.9%
7 43
 
0.9%
1 10
 
0.2%
0 1
 
< 0.1%
Other Punctuation
ValueCountFrequency (%)
/ 347330
64.7%
, 189078
35.2%
& 62
 
< 0.1%
Space Separator
ValueCountFrequency (%)
1318866
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 98737
100.0%
Open Punctuation
ValueCountFrequency (%)
( 94560
100.0%
Close Punctuation
ValueCountFrequency (%)
) 94560
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 12209438
85.0%
Common 2148113
 
15.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
N 1940321
15.9%
O 1760625
14.4%
E 1391709
11.4%
W 1022116
8.4%
A 929751
7.6%
R 778492
 
6.4%
P 693913
 
5.7%
T 691927
 
5.7%
H 477098
 
3.9%
U 365508
 
3.0%
Other values (16) 2157978
17.7%
Common
ValueCountFrequency (%)
1318866
61.4%
/ 347330
 
16.2%
, 189078
 
8.8%
- 98737
 
4.6%
( 94560
 
4.4%
) 94560
 
4.4%
6 4700
 
0.2%
& 62
 
< 0.1%
9 62
 
< 0.1%
3 61
 
< 0.1%
Other values (4) 97
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 14357551
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
N 1940321
13.5%
O 1760625
12.3%
E 1391709
9.7%
1318866
 
9.2%
W 1022116
 
7.1%
A 929751
 
6.5%
R 778492
 
5.4%
P 693913
 
4.8%
T 691927
 
4.8%
H 477098
 
3.3%
Other values (30) 3352733
23.4%

Status
Categorical

IMBALANCE 

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size3.8 MiB
IC
395890 
AO
53969 
AA
43282 
JA
 
1585
JO
 
684

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters990820
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowAO
2nd rowIC
3rd rowIC
4th rowIC
5th rowIC

Common Values

ValueCountFrequency (%)
IC 395890
79.9%
AO 53969
 
10.9%
AA 43282
 
8.7%
JA 1585
 
0.3%
JO 684
 
0.1%

Length

2024-05-13T09:54:14.236791image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-13T09:54:14.482738image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
ValueCountFrequency (%)
ic 395890
79.9%
ao 53969
 
10.9%
aa 43282
 
8.7%
ja 1585
 
0.3%
jo 684
 
0.1%

Most occurring characters

ValueCountFrequency (%)
I 395890
40.0%
C 395890
40.0%
A 142118
 
14.3%
O 54653
 
5.5%
J 2269
 
0.2%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 990820
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
I 395890
40.0%
C 395890
40.0%
A 142118
 
14.3%
O 54653
 
5.5%
J 2269
 
0.2%

Most occurring scripts

ValueCountFrequency (%)
Latin 990820
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
I 395890
40.0%
C 395890
40.0%
A 142118
 
14.3%
O 54653
 
5.5%
J 2269
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 990820
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
I 395890
40.0%
C 395890
40.0%
A 142118
 
14.3%
O 54653
 
5.5%
J 2269
 
0.2%

Status Desc
Categorical

IMBALANCE 

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size3.8 MiB
Invest Cont
395890 
Adult Other
53969 
Adult Arrest
43282 
Juv Arrest
 
1585
Juv Other
 
684

Length

Max length12
Median length11
Mean length11.081405
Min length9

Characters and Unicode

Total characters5489839
Distinct characters17
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowAdult Other
2nd rowInvest Cont
3rd rowInvest Cont
4th rowInvest Cont
5th rowInvest Cont

Common Values

ValueCountFrequency (%)
Invest Cont 395890
79.9%
Adult Other 53969
 
10.9%
Adult Arrest 43282
 
8.7%
Juv Arrest 1585
 
0.3%
Juv Other 684
 
0.1%

Length

2024-05-13T09:54:14.903514image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-13T09:54:15.215172image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
ValueCountFrequency (%)
invest 395890
40.0%
cont 395890
40.0%
adult 97251
 
9.8%
other 54653
 
5.5%
arrest 44867
 
4.5%
juv 2269
 
0.2%

Most occurring characters

ValueCountFrequency (%)
t 988551
18.0%
n 791780
14.4%
e 495410
9.0%
495410
9.0%
s 440757
8.0%
v 398159
7.3%
I 395890
7.2%
o 395890
7.2%
C 395890
7.2%
r 144387
 
2.6%
Other values (7) 547715
10.0%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 4003609
72.9%
Uppercase Letter 990820
 
18.0%
Space Separator 495410
 
9.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
t 988551
24.7%
n 791780
19.8%
e 495410
12.4%
s 440757
11.0%
v 398159
9.9%
o 395890
9.9%
r 144387
 
3.6%
u 99520
 
2.5%
d 97251
 
2.4%
l 97251
 
2.4%
Uppercase Letter
ValueCountFrequency (%)
I 395890
40.0%
C 395890
40.0%
A 142118
 
14.3%
O 54653
 
5.5%
J 2269
 
0.2%
Space Separator
ValueCountFrequency (%)
495410
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 4994429
91.0%
Common 495410
 
9.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
t 988551
19.8%
n 791780
15.9%
e 495410
9.9%
s 440757
8.8%
v 398159
8.0%
I 395890
7.9%
o 395890
7.9%
C 395890
7.9%
r 144387
 
2.9%
A 142118
 
2.8%
Other values (6) 405597
8.1%
Common
ValueCountFrequency (%)
495410
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 5489839
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
t 988551
18.0%
n 791780
14.4%
e 495410
9.0%
495410
9.0%
s 440757
8.0%
v 398159
7.3%
I 395890
7.2%
o 395890
7.2%
C 395890
7.2%
r 144387
 
2.6%
Other values (7) 547715
10.0%
Distinct56610
Distinct (%)11.4%
Missing0
Missing (%)0.0%
Memory size3.8 MiB
2024-05-13T09:54:16.003311image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Length

Max length40
Median length39
Mean length35.630655
Min length1

Characters and Unicode

Total characters17651783
Distinct characters37
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique14739 ?
Unique (%)3.0%

Sample

1st row1100 W 39TH PL
2nd row700 S HILL ST
3rd row5400 CORTEEN PL
4th row14400 TITUS ST
5th row700 S BROADWAY
ValueCountFrequency (%)
st 165310
 
10.3%
av 143224
 
9.0%
bl 92379
 
5.8%
s 64582
 
4.0%
w 57719
 
3.6%
n 29570
 
1.8%
e 24881
 
1.6%
dr 19045
 
1.2%
pl 14273
 
0.9%
600 11544
 
0.7%
Other values (6918) 976735
61.1%
2024-05-13T09:54:17.105160image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
11531055
65.3%
0 884414
 
5.0%
A 481664
 
2.7%
S 394239
 
2.2%
T 379334
 
2.1%
E 350265
 
2.0%
N 318176
 
1.8%
L 310183
 
1.8%
R 281846
 
1.6%
O 263859
 
1.5%
Other values (27) 2456748
 
13.9%

Most occurring categories

ValueCountFrequency (%)
Space Separator 11531055
65.3%
Uppercase Letter 4336009
 
24.6%
Decimal Number 1784719
 
10.1%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A 481664
11.1%
S 394239
 
9.1%
T 379334
 
8.7%
E 350265
 
8.1%
N 318176
 
7.3%
L 310183
 
7.2%
R 281846
 
6.5%
O 263859
 
6.1%
V 214926
 
5.0%
I 177643
 
4.1%
Other values (16) 1163874
26.8%
Decimal Number
ValueCountFrequency (%)
0 884414
49.6%
1 229903
 
12.9%
2 110989
 
6.2%
3 89242
 
5.0%
4 86290
 
4.8%
5 86250
 
4.8%
6 83681
 
4.7%
7 76969
 
4.3%
8 75766
 
4.2%
9 61215
 
3.4%
Space Separator
ValueCountFrequency (%)
11531055
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 13315774
75.4%
Latin 4336009
 
24.6%

Most frequent character per script

Latin
ValueCountFrequency (%)
A 481664
11.1%
S 394239
 
9.1%
T 379334
 
8.7%
E 350265
 
8.1%
N 318176
 
7.3%
L 310183
 
7.2%
R 281846
 
6.5%
O 263859
 
6.1%
V 214926
 
5.0%
I 177643
 
4.1%
Other values (16) 1163874
26.8%
Common
ValueCountFrequency (%)
11531055
86.6%
0 884414
 
6.6%
1 229903
 
1.7%
2 110989
 
0.8%
3 89242
 
0.7%
4 86290
 
0.6%
5 86250
 
0.6%
6 83681
 
0.6%
7 76969
 
0.6%
8 75766
 
0.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 17651783
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
11531055
65.3%
0 884414
 
5.0%
A 481664
 
2.7%
S 394239
 
2.2%
T 379334
 
2.1%
E 350265
 
2.0%
N 318176
 
1.8%
L 310183
 
1.8%
R 281846
 
1.6%
O 263859
 
1.5%
Other values (27) 2456748
 
13.9%

LAT
Real number (ℝ)

Distinct5307
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean33.917114
Minimum0
Maximum34.3343
Zeros2274
Zeros (%)0.5%
Negative0
Negative (%)0.0%
Memory size3.8 MiB
2024-05-13T09:54:17.467150image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile33.9118
Q134.0112
median34.0583
Q334.163475
95-th percentile34.2536
Maximum34.3343
Range34.3343
Interquartile range (IQR)0.152275

Descriptive statistics

Standard deviation2.3059019
Coefficient of variation (CV)0.067986382
Kurtosis211.8544
Mean33.917114
Median Absolute Deviation (MAD)0.0643
Skewness-14.606306
Sum16802877
Variance5.3171833
MonotonicityNot monotonic
2024-05-13T09:54:18.352368image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
34.1016 2800
 
0.6%
34.098 2276
 
0.5%
0 2274
 
0.5%
34.2012 2239
 
0.5%
34.1939 1730
 
0.3%
34.1867 1610
 
0.3%
34.1649 1516
 
0.3%
34.1903 1305
 
0.3%
34.0998 1287
 
0.3%
34.1938 1249
 
0.3%
Other values (5297) 477124
96.3%
ValueCountFrequency (%)
0 2274
0.5%
33.7061 1
 
< 0.1%
33.7064 9
 
< 0.1%
33.7065 2
 
< 0.1%
33.707 46
 
< 0.1%
33.7071 1
 
< 0.1%
33.7074 2
 
< 0.1%
33.7076 1
 
< 0.1%
33.7079 35
 
< 0.1%
33.7087 15
 
< 0.1%
ValueCountFrequency (%)
34.3343 1
 
< 0.1%
34.333 1
 
< 0.1%
34.3297 2
< 0.1%
34.3293 1
 
< 0.1%
34.3292 2
< 0.1%
34.3289 1
 
< 0.1%
34.3287 2
< 0.1%
34.3286 1
 
< 0.1%
34.3283 4
< 0.1%
34.3282 3
< 0.1%

LON
Real number (ℝ)

Distinct4934
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-117.81128
Minimum-118.6676
Maximum0
Zeros2274
Zeros (%)0.5%
Negative493136
Negative (%)99.5%
Memory size3.8 MiB
2024-05-13T09:54:18.933967image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum-118.6676
5-th percentile-118.56606
Q1-118.4301
median-118.3224
Q3-118.274
95-th percentile-118.2189
Maximum0
Range118.6676
Interquartile range (IQR)0.1561

Descriptive statistics

Standard deviation8.0008436
Coefficient of variation (CV)-0.067912375
Kurtosis212.79242
Mean-117.81128
Median Absolute Deviation (MAD)0.0638
Skewness14.654509
Sum-58364885
Variance64.013498
MonotonicityNot monotonic
2024-05-13T09:54:19.253927image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-118.2739 3619
 
0.7%
-118.2827 3205
 
0.6%
0 2274
 
0.5%
-118.2652 2067
 
0.4%
-118.2783 2017
 
0.4%
-118.2871 1988
 
0.4%
-118.2915 1940
 
0.4%
-118.2916 1912
 
0.4%
-118.3089 1863
 
0.4%
-118.309 1778
 
0.4%
Other values (4924) 472747
95.4%
ValueCountFrequency (%)
-118.6676 4
< 0.1%
-118.6673 5
< 0.1%
-118.6672 2
 
< 0.1%
-118.6665 1
 
< 0.1%
-118.6663 1
 
< 0.1%
-118.6661 3
< 0.1%
-118.6652 6
< 0.1%
-118.6644 2
 
< 0.1%
-118.6642 1
 
< 0.1%
-118.6634 2
 
< 0.1%
ValueCountFrequency (%)
0 2274
0.5%
-118.1554 3
 
< 0.1%
-118.156 16
 
< 0.1%
-118.1565 1
 
< 0.1%
-118.1574 2
 
< 0.1%
-118.158 1
 
< 0.1%
-118.1581 1
 
< 0.1%
-118.1584 1
 
< 0.1%
-118.1585 3
 
< 0.1%
-118.1586 1
 
< 0.1%

Interactions

2024-05-13T09:53:53.731187image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-05-13T09:53:34.946437image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-05-13T09:53:37.039148image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-05-13T09:53:40.151871image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-05-13T09:53:42.269366image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-05-13T09:53:45.514693image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-05-13T09:53:48.043382image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-05-13T09:53:50.851749image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-05-13T09:53:54.091423image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-05-13T09:53:35.253357image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-05-13T09:53:37.361214image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-05-13T09:53:40.445228image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-05-13T09:53:42.641076image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-05-13T09:53:45.810151image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-05-13T09:53:48.319359image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-05-13T09:53:51.301971image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-05-13T09:53:54.448303image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-05-13T09:53:35.546122image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-05-13T09:53:37.752344image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-05-13T09:53:40.705624image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-05-13T09:53:43.204934image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-05-13T09:53:46.086097image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-05-13T09:53:48.617052image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-05-13T09:53:51.625629image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-05-13T09:53:54.807181image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-05-13T09:53:35.785382image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-05-13T09:53:38.161250image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-05-13T09:53:40.976357image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-05-13T09:53:43.666944image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-05-13T09:53:46.327158image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-05-13T09:53:48.932022image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-05-13T09:53:51.975426image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-05-13T09:53:55.332172image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-05-13T09:53:36.021931image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-05-13T09:53:38.547065image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-05-13T09:53:41.216189image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-05-13T09:53:44.022083image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-05-13T09:53:46.573947image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-05-13T09:53:49.259930image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-05-13T09:53:52.289751image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-05-13T09:53:55.648211image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-05-13T09:53:36.276445image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-05-13T09:53:39.016804image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-05-13T09:53:41.492951image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-05-13T09:53:44.489991image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-05-13T09:53:46.839477image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-05-13T09:53:49.586869image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-05-13T09:53:52.586261image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-05-13T09:53:55.914603image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-05-13T09:53:36.521096image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-05-13T09:53:39.353571image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-05-13T09:53:41.736120image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-05-13T09:53:44.822246image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-05-13T09:53:47.098415image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-05-13T09:53:50.212715image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-05-13T09:53:53.132227image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-05-13T09:53:56.183959image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-05-13T09:53:36.773244image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-05-13T09:53:39.802248image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-05-13T09:53:41.985062image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-05-13T09:53:45.138964image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-05-13T09:53:47.609590image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-05-13T09:53:50.571522image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-05-13T09:53:53.406951image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Missing values

2024-05-13T09:53:56.784729image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-05-13T09:53:58.308742image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

DR_NODATE OCCTIME OCCAREAAREA NAMERpt Dist NoCrm CdCrm Cd DescMocodesVict AgeVict SexVict DescentPremis DescWeapon Used CdWeapon DescStatusStatus DescLOCATIONLATLON
01030446808/01/202022:303Southwest377624BATTERY - SIMPLE ASSAULT0444 091336FBSINGLE FAMILY DWELLING400.0STRONG-ARM (HANDS, FIST, FEET OR BODILY FORCE)AOAdult Other1100 W 39TH PL34.0141-118.2978
119010108601/01/202003:301Central163624BATTERY - SIMPLE ASSAULT0416 1822 141425MHSIDEWALK500.0UNKNOWN WEAPON/OTHER WEAPONICInvest Cont700 S HILL ST34.0459-118.2545
219150150501/01/202017:3015N Hollywood1543745VANDALISM - MISDEAMEANOR ($399 OR UNDER)0329 140276FWMULTI-UNIT DWELLING (APARTMENT, DUPLEX, ETC)500.0UNKNOWN WEAPON/OTHER WEAPONICInvest Cont5400 CORTEEN PL34.1685-118.4019
319192126901/01/202004:1519Mission1998740VANDALISM - FELONY ($400 & OVER, ALL CHURCH VANDALISMS)032931XXBEAUTY SUPPLY STORE500.0UNKNOWN WEAPON/OTHER WEAPONICInvest Cont14400 TITUS ST34.2198-118.4468
420010050101/01/202000:301Central163121RAPE, FORCIBLE0413 1822 1262 141525FHNIGHT CLUB (OPEN EVENINGS ONLY)500.0UNKNOWN WEAPON/OTHER WEAPONICInvest Cont700 S BROADWAY34.0452-118.2534
520010050202/01/202013:151Central161442SHOPLIFTING - PETTY THEFT ($950 & UNDER)1402 2004 0344 038723MHDEPARTMENT STORE500.0UNKNOWN WEAPON/OTHER WEAPONICInvest Cont700 S FIGUEROA ST34.0483-118.2631
620010050404/01/202000:401Central155946OTHER MISCELLANEOUS CRIME1402 03920XXPOLICE FACILITY500.0UNKNOWN WEAPON/OTHER WEAPONICInvest Cont200 E 6TH ST34.0448-118.2474
720010050704/01/202002:001Central101341THEFT-GRAND ($950.01 & OVER)EXCPT,GUNS,FOWL,LIVESTK,PROD1822 0344 140223MBMULTI-UNIT DWELLING (APARTMENT, DUPLEX, ETC)500.0UNKNOWN WEAPON/OTHER WEAPONICInvest Cont700 BERNARD ST34.0677-118.2398
820010050904/01/202022:001Central192330BURGLARY FROM VEHICLE1822 1414 0344 130729MASTREET306.0ROCK/THROWN OBJECTICInvest Cont15TH34.0359-118.2648
920010051005/01/202009:551Central111930CRIMINAL THREATS - NO WEAPON DISPLAYED0421 090635MOPARKING LOT511.0VERBAL THREATICInvest Cont800 N ALAMEDA ST34.0615-118.2412
DR_NODATE OCCTIME OCCAREAAREA NAMERpt Dist NoCrm CdCrm Cd DescMocodesVict AgeVict SexVict DescentPremis DescWeapon Used CdWeapon DescStatusStatus DescLOCATIONLATLON
49540022010654113/02/202217:451Central145624BATTERY - SIMPLE ASSAULT0400 0416 0444 1266 091326MWHOTEL400.0STRONG-ARM (HANDS, FIST, FEET OR BODILY FORCE)AOAdult Other100 E 5TH ST34.0471-118.2474
49540122150775530/03/202218:0015N Hollywood1519740VANDALISM - FELONY ($400 & OVER, ALL CHURCH VANDALISMS)1300 032949FHSTREET500.0UNKNOWN WEAPON/OTHER WEAPONICInvest Cont6700 CLEON AV34.1921-118.3682
49540222121270428/05/202214:001277th Street1255745VANDALISM - MISDEAMEANOR ($399 OR UNDER)0329 130066FHSTREET500.0UNKNOWN WEAPON/OTHER WEAPONICInvest ContHALLDALE AV33.9746-118.3024
49540322080506427/01/202219:008West LA884510VEHICLE - STOLEN00XXSTREET500.0UNKNOWN WEAPON/OTHER WEAPONICInvest Cont2300 S BENTLEY AV34.0392-118.4358
49540422100646207/03/202218:0010West Valley1003901VIOLATION OF RESTRAINING ORDER2038 0913 056173MOMULTI-UNIT DWELLING (APARTMENT, DUPLEX, ETC)500.0UNKNOWN WEAPON/OTHER WEAPONICInvest Cont18500 ROSCOE BL34.2208-118.5361
49540522190728320/03/202201:0019Mission1901341THEFT-GRAND ($950.01 & OVER)EXCPT,GUNS,FOWL,LIVESTK,PROD0344 182237MWPARKING LOT500.0UNKNOWN WEAPON/OTHER WEAPONICInvest Cont14000 BALBOA BL34.3226-118.4905
49540622190614523/02/202212:1019Mission1985421THEFT FROM MOTOR VEHICLE - ATTEMPT1822 140248FHPARKING LOT500.0UNKNOWN WEAPON/OTHER WEAPONICInvest Cont8400 VAN NUYS BL34.2229-118.4487
49540722100550709/02/202215:3010West Valley1024510VEHICLE - STOLEN00XXPARKING LOT500.0UNKNOWN WEAPON/OTHER WEAPONICInvest Cont18800 SHERMAN WY34.2011-118.5426
49540822110547708/02/202220:0011Northeast1171510VEHICLE - STOLEN00XXSTREET500.0UNKNOWN WEAPON/OTHER WEAPONICInvest Cont4000 FOUNTAIN AV34.0958-118.2787
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